Our Random Forest model uses the following predictors to predict overdose deaths:
- Prescription Rate of each of 17 opioids, calculated from Medicare Data
- Overall prescription rate for all opioids, obtained from the CDC
- Various measures obtained from the Census Bureau:
- Labor Force Participation Rate
- Unemployment Rate
- Median Income
- Percentage of Population with Less Than a High School Education
- Percent of the Population Non-Hispanic White
- Percentage of the Population Living in a Rural Area
- Percentage of Males who are Unmarried
- Share of the vote in the 2016 election going to Donald Trump
- Number of Suboxone Prescribers per 100,000 Residents, calculated from Medicare Data
- Number of Facilities Providing Some Medication Assisted Treatment per 100,000 residents, obtained from The Foundation for AIDS Research
- Region of the country, as defined by the Census Bureau.
- 37 predictors
- Data based on year 2013 - 2016, mostly using Census Data
- Blue curve represents our model’s regression
- The red line represents the predicted values if our model had a perfect prediction rate
- This model would likely have produced stronger predictions if we had all of the predictor variable data available at a county level
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'


- Suboxone prescribers are most important predictor (obviously)
- Other important and more interesting predictors were:
- If state was in New England
- Labor force participation rate more than twice as important as unemployment rate
- Percent of state that voted for Trump in 2016 general election
- Percent of state that is white
- We predict these predictors would be more pronounced if we had all of this data on a county level
Stochastic Gradient Boosting model:
- 159 samples
- Same 37 predictors
- Same years (2013 - 2016)
- Similar performance as Random Forest
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

Conclusions:
- The overdose death rate has not leveled off yet
- Per capita prescription decrease has not lessened overdose deaths
- There is a pareto effect by both opioid prescribers and by specialty
- Using a model to make predictions for all states is difficult b/c of the diversity of states
- Key trends: Opioid death rates are associated with areas that are:
- Rural
- White
- Working class
- Pro-Trump in ’16 election
- Experiencing dropping labor market participation rates
- Prescribing Oxycodone